{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Face Rank\n",
    "\n",
    "数学与统计学院 \n",
    "\n",
    "江金阳 2017301000090 周睿涵 2018302010038\n",
    "\n",
    "作业训练的是一个迷你的VGG网络，用来给人的颜值打分，颜值越高分数越高（0-9分）。由于数据集质量的原因目前的训练结果时而不尽人意，但整个代码框架和思路已经基本上完善好了，如有必要我可再重新制作数据集训练或添加其他功能。\n",
    "\n",
    "这个项目中我使用的训练集、验证集和测试集中没有相同的样本，模型可以对没有见过的面孔打分。从预测的情况来看，由于训练集普遍是笑的正面登记照，尽管我用拉伸变形进行了数据增强，模型仍然对表情或光照有一定的敏感性，或许以后在这个问题上我应该考虑选择用脸的特征点来做，而不是直接用图像来做。\n",
    "\n",
    "\n",
    "**分工明细:**\n",
    "\n",
    "| **Chapter** | **Jiang Jinyang** | **Zhou Ruihan** |\n",
    "|:--------|:--------|:--------|\n",
    "|0 打印硬件信息|  |$\\checkmark$|\n",
    "|1 重置数据文件夹|  |$\\checkmark$|\n",
    "|2 准备数据集|  |$\\checkmark$|\n",
    "|3 生成数据集|$\\checkmark$|$\\checkmark$|\n",
    "|4 定义神经网络|$\\checkmark$|  |\n",
    "|5 参数管理|  |$\\checkmark$|\n",
    "|6 训练过程|$\\checkmark$|  |\n",
    "|7 测试过程|$\\checkmark$|  |\n",
    "|8 静态颜值预测|$\\checkmark$|$\\checkmark$|\n",
    "|9 动态颜值预测|$\\checkmark$|  |\n",
    "\n",
    "**说明:**\n",
    "\n",
    "上述0、1、2、8、9项可以单独运行，运行3-7时建议顺序运行\n",
    "\n",
    "**项目所有依赖如下:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 项目的所有依赖项，若顺序运行可不执行本窗口\n",
    "import shutil\n",
    "import torch\n",
    "import os\n",
    "import xlrd\n",
    "from PIL import Image\n",
    "import face_recognition\n",
    "from tqdm.notebook import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import argparse\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "from torch.optim.lr_scheduler import StepLR\n",
    "from torch.utils.data.sampler import SubsetRandomSampler\n",
    "from torchvision import transforms, datasets\n",
    "from tensorboardX import SummaryWriter\n",
    "\n",
    "# 仅用于爬虫的依赖项\n",
    "from bs4 import BeautifulSoup\n",
    "import urllib.request\n",
    "import requests\n",
    "import time\n",
    "import json\n",
    "import sys\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0 打印硬件信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num of GPU: 1\n",
      "Cuda is available: True\n",
      "Working device: cuda:0\n",
      "GPU info: GeForce GTX 1050 Ti\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "print(f'Num of GPU: {torch.cuda.device_count()}')\n",
    "print(f'Cuda is available: {torch.cuda.is_available()}')\n",
    "print(f'Working device: {torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")}')\n",
    "print(f'GPU info: {torch.cuda.get_device_name(0)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 重置数据文件夹\n",
    "\n",
    "清除所有工作文件夹内数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./original_train_set'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shutil.rmtree('./original_train_set')\n",
    "shutil.copytree('./reset/original_train_set','./original_train_set')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./original_test_set'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shutil.rmtree('./original_test_set')\n",
    "shutil.copytree('./reset/original_test_set','./original_test_set')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./face_train_set'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shutil.rmtree('./face_train_set')\n",
    "shutil.copytree('./reset/face_train_set','./face_train_set')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./face_test_set'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shutil.rmtree('./face_test_set')\n",
    "shutil.copytree('./reset/face_test_set','./face_test_set')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 准备数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import xlrd\n",
    "import shutil\n",
    "from PIL import Image\n",
    "import face_recognition\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "import urllib.request\n",
    "import requests\n",
    "import time\n",
    "import json\n",
    "import sys\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 从SCUT-FBP5500_v2导入数据\n",
    "\n",
    "SCUT-FBP5500_v2是华南理工大学人机交互智能实验室2017年制作的数据集，其中包含亚洲、欧美男女性脸部图片及对应打分，命名规则为亚洲男性（AM）、亚洲女性（AF）、欧美男性（CM）、欧美女性（CF），本项目暂时只使用了其中女性的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def score2rank(score):\n",
    "    '''\n",
    "    tansform real score to discrete ranks\n",
    "    '''\n",
    "    standard = [2.2,\n",
    "                2.416667,\n",
    "                2.583333,\n",
    "                2.7,\n",
    "                2.833333,\n",
    "                3.016667,\n",
    "                3.316667,\n",
    "                3.7,\n",
    "                4.033333,\n",
    "               ]\n",
    "    if score <= standard[0]:\n",
    "        return 0\n",
    "    elif score > standard[0] and score <= standard[1]:\n",
    "        return 1\n",
    "    elif score > standard[1] and score <= standard[2]:\n",
    "        return 2\n",
    "    elif score > standard[2] and score <= standard[3]:\n",
    "        return 3\n",
    "    elif score > standard[3] and score <= standard[4]:\n",
    "        return 4\n",
    "    elif score > standard[4] and score <= standard[5]:\n",
    "        return 5\n",
    "    elif score > standard[5] and score <= standard[6]:\n",
    "        return 6\n",
    "    elif score > standard[6] and score <= standard[7]:\n",
    "        return 7\n",
    "    elif score > standard[7] and score <= standard[8]:\n",
    "        return 8\n",
    "    elif score > standard[8]:\n",
    "        return 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating original train set:\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dc6ae355590647b88605418192059d72",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3300.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generating original test set:\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "27777b067ddd4a4b8bf5e79bcad10c19",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=2200.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "data = xlrd.open_workbook('./train.xlsx')\n",
    "table = data.sheets()[0]\n",
    "nrows = table.nrows\n",
    "\n",
    "print('Generating original train set:')\n",
    "for i in tqdm(range(nrows)):\n",
    "    image_name, score = table.row_values(i)\n",
    "    if 'F' in image_name:\n",
    "        srcpath = './original_set/' + image_name\n",
    "        dstpath = './original_train_set/'+ str(score2rank(score)) + '/' + image_name        \n",
    "        shutil.copy(srcpath, dstpath)\n",
    "    \n",
    "data = xlrd.open_workbook('./test.xlsx')\n",
    "table = data.sheets()[0]\n",
    "nrows = table.nrows\n",
    "\n",
    "print('Generating original test set:')\n",
    "for i in tqdm(range(nrows)):\n",
    "    image_name, score = table.row_values(i)\n",
    "    if 'F' in image_name:\n",
    "        srcpath = './original_set/' + image_name\n",
    "        dstpath = './original_test_set/'+ str(score2rank(score)) + '/' + image_name        \n",
    "        shutil.copy(srcpath, dstpath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"float: center;\" src=\"./jupyter_image/dataprepare0.png\" width=\"60%\"> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 用爬虫补充数据集\n",
    "\n",
    "从SCUT-FBP5500_v2数据集获取的数据并不一定符合我们当前的审美标准，本段允许用户根据自己的喜好添加数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#爬取目标网站url\n",
    "CRAWL_TARGET_URL = 'https://cn.bing.com/images/async?q=%s&first=%d&count=%d&relp=%d&lostate=r&mmasync=1'\n",
    "#每次抓取图片数量(35是此网页每次翻页请求数量)\n",
    "NUMS_PER_CRAWL = 35\n",
    "#抓取图片最小大小(单位字节)，小于此值抛弃\n",
    "MIN_IMAGE_SIZE = 10\n",
    "\n",
    "def get_image(url, path, count):\n",
    "    try:\n",
    "        u = urllib.request.urlopen(url, timeout=5)\n",
    "        t = u.read()\n",
    "        if sys.getsizeof(t) < MIN_IMAGE_SIZE:\n",
    "            return -1\n",
    "    except Exception as e:\n",
    "        #print(url, e)\n",
    "        return -2\n",
    "    #提取图片格式\n",
    "    frmt = url[url.rfind('.'):]\n",
    "    p = re.compile(\"^\\\\.[a-zA-Z]+\")\n",
    "    m = p.match(frmt)\n",
    "    frmt = m.group(0)\n",
    "    try:\n",
    "        if not os.path.exists(path):\n",
    "            os.mkdir(path)\n",
    "        f = open(os.path.join(path, str(count)+frmt), 'wb')\n",
    "        f.write(t)\n",
    "        f.close()\n",
    "    except Exception as e:\n",
    "        #print(os.path.join(path, str(count)+frmt), e)\n",
    "        return -3\n",
    "    return 0\n",
    "\n",
    "\n",
    "def crawl_data(info, num, save_path):\n",
    "    first = 0\n",
    "    count = 0\n",
    "    #创建一个会话\n",
    "    s = requests.Session()\n",
    "    index=len(os.listdir(save_path))#文件中原有图片数\n",
    "    while count < num:\n",
    "        u = CRAWL_TARGET_URL%(info, first, NUMS_PER_CRAWL, NUMS_PER_CRAWL)\n",
    "        #3.05s为发送超时时间，10s为接收到数据超时时间\n",
    "        req = s.get(url =u, timeout=(3.05, 10))\n",
    "        bf = BeautifulSoup(req.text, \"html.parser\")\n",
    "        imgtags = bf.find_all(\"a\", class_ = \"iusc\")\n",
    "        for e in imgtags:\n",
    "            if count == num:\n",
    "                return False\n",
    "            urldict = json.loads(e.get('m'))\n",
    "            if get_image(urldict[\"murl\"], save_path, index) < 0:\n",
    "                continue\n",
    "            print(\"Downloaded %d picture\"%(count+1))\n",
    "            sys.stdout.flush()\n",
    "            count = count+1\n",
    "            index = index+1\n",
    "            time.sleep(0.01)\n",
    "        first = first + NUMS_PER_CRAWL\n",
    "        time.sleep(0.1)\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading label: 黑猩猩 Rank: 0\n",
      "Downloaded 1 picture\n",
      "Downloaded 2 picture\n",
      "Downloaded 3 picture\n",
      "Downloaded 4 picture\n",
      "Downloaded 5 picture\n",
      "Downloaded 6 picture\n",
      "Downloaded 7 picture\n",
      "Downloaded 8 picture\n",
      "Downloaded 9 picture\n",
      "Downloaded 10 picture\n",
      "Downloading label: 抖音 美女 Rank: 7\n",
      "Downloaded 1 picture\n",
      "Downloaded 2 picture\n",
      "Downloaded 3 picture\n",
      "Downloaded 4 picture\n",
      "Downloaded 5 picture\n",
      "Downloaded 6 picture\n",
      "Downloaded 7 picture\n",
      "Downloaded 8 picture\n",
      "Downloaded 9 picture\n",
      "Downloaded 10 picture\n",
      "Downloading label: 最美面孔 女 亚洲 Rank: 9\n",
      "Downloaded 1 picture\n",
      "Downloaded 2 picture\n",
      "Downloaded 3 picture\n",
      "Downloaded 4 picture\n",
      "Downloaded 5 picture\n",
      "Downloaded 6 picture\n",
      "Downloaded 7 picture\n",
      "Downloaded 8 picture\n",
      "Downloaded 9 picture\n",
      "Downloaded 10 picture\n",
      "Downloading label: 亚洲最美10大女明星 Rank: 9\n",
      "Downloaded 1 picture\n",
      "Downloaded 2 picture\n",
      "Downloaded 3 picture\n",
      "Downloaded 4 picture\n",
      "Downloaded 5 picture\n",
      "Downloaded 6 picture\n",
      "Downloaded 7 picture\n",
      "Downloaded 8 picture\n",
      "Downloaded 9 picture\n",
      "Downloaded 10 picture\n"
     ]
    }
   ],
   "source": [
    "# key_words中每条包含：爬取的关键词，爬取数量，用户对这类图片的给分\n",
    "key_words=[['黑猩猩',10,'0'],\n",
    "           ['抖音 美女',10,'7'],\n",
    "           ['最美面孔 女 亚洲',10,'9'],\n",
    "           ['亚洲最美10大女明星',10,'9'],\n",
    "          ]\n",
    "\n",
    "for i in range(len(key_words)):\n",
    "    info = key_words[i][0]\n",
    "    num = key_words[i][1]\n",
    "    rank =  key_words[i][2]\n",
    "    save_path = './original_train_set/' + rank\n",
    "    print(f'Downloading label: {info} Rank: {rank}')\n",
    "    if crawl_data(info, num, save_path):\n",
    "        i = i+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"float: center;\" src=\"./jupyter_image/dataprepare1.png\" width=\"60%\"> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 捕获人脸并统一图片尺寸\n",
    "\n",
    "使用face_recognition库中的face_locations接口获取图片中人脸上下左右的指标范围，再截取人脸并变换成统一尺寸，在本文中，图片尺寸被统一为 $128\\times128\\times3$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_and_resize_face(original,face):\n",
    "    # Load the jpg file into a numpy array\n",
    "    image = face_recognition.load_image_file(original)\n",
    "    # Find all the faces in the image\n",
    "    face_locations = face_recognition.face_locations(image)\n",
    "    for face_location in face_locations:\n",
    "        top, right, bottom, left = face_location\n",
    "        face_image = image[top:bottom, left:right]\n",
    "        pil_image = Image.fromarray(face_image)\n",
    "        pil_image = pil_image.resize((128, 128))\n",
    "        pil_image.save(face)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating face train set:\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2da43524c394aae89f2e8d1db80ef93",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generating face test set:\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "782b643e14724f1f9db1e98dfe634b2d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print('Generating face train set:')            \n",
    "folder_list = os.listdir('./original_train_set')\n",
    "for folder in tqdm(folder_list):\n",
    "    image_list = os.listdir('./original_train_set/' + folder)\n",
    "    for image in image_list:\n",
    "        id_tag = image.find(\".\") # 之所以写这一步是为了避免出现非.jpg后缀的图片文件\n",
    "        image_name = image[0:id_tag]\n",
    "        original = './original_train_set/' + folder + '/' + image\n",
    "        face =  './face_train_set/' + folder + '/' + image_name + '.jpg'\n",
    "        try:\n",
    "            find_and_resize_face(original,face)\n",
    "        except:\n",
    "            print(\"fail\")\n",
    "\n",
    "print('Generating face test set:')\n",
    "folder_list = os.listdir('./original_test_set')\n",
    "for folder in tqdm(folder_list):\n",
    "    image_list = os.listdir('./original_test_set/' + folder)\n",
    "    for image in image_list:\n",
    "        id_tag = image.find(\".\")\n",
    "        image_name = image[0:id_tag]\n",
    "        original = './original_test_set/' + folder + '/' + image\n",
    "        face =  './face_test_set/' + folder + '/' + image_name + '.jpg'\n",
    "        try:\n",
    "            find_and_resize_face(original,face)\n",
    "        except:\n",
    "            print(\"fail\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"float: center;\" src=\"./jupyter_image/dataprepare2.png\" width=\"60%\"> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 生成数据集\n",
    "\n",
    "torchvision.datasets.ImageFolder是一个通用的数据加载器,允许用户制作一个类似CIFAR10的数据集，并通过DataLoader使用它\n",
    "\n",
    "我们将训练集、验证集的生成封装成了函数get_train_valid_loader，将测试集的生成封装成了函数get_test_loader，他们分别返回两个和一个DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torchvision\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.utils.data.sampler import SubsetRandomSampler\n",
    "from torchvision import transforms, datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.1 生成训练集和验证集\n",
    "\n",
    "训练集和验证集从原始训练集图片文件夹生成，按一定比例（valid_ratio）划分为训练集和验证集\n",
    "\n",
    "由于我们不便于收集比较大的数据集，样本规模在$10^4$个以下，训练时容易发生数据量引发的过拟合，因此在训练集上进行了数据增强，对图片以一定的概率水平翻转、旋转、仿射变换，在多轮训练中，相当于将数据集扩大了若干倍"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_train_valid_loader(data_dir,\n",
    "                           batch_size,\n",
    "                           random_seed,\n",
    "                           valid_ratio=0.1,\n",
    "                           shuffle=True,\n",
    "                           num_workers=4,\n",
    "                           pin_memory=False):\n",
    "\n",
    "    # dataset augmentation for train_set\n",
    "    random_choice = transforms.RandomChoice([\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.RandomRotation(30),\n",
    "        transforms.RandomAffine(degrees=0, shear=(10,10)),\n",
    "        ])\n",
    "    \n",
    "    transform_train = transforms.Compose([\n",
    "        transforms.RandomApply([random_choice], p=0.6),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
    "    ])\n",
    "    \n",
    "    # transform for valid_set\n",
    "    transform_valid = transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
    "    ])\n",
    "\n",
    "    # load the dataset\n",
    "    train_set = datasets.ImageFolder(root=data_dir, transform=transform_train)\n",
    "    valid_set = datasets.ImageFolder(root=data_dir, transform=transform_valid)\n",
    "\n",
    "    num_train = len(train_set)\n",
    "    indices = list(range(num_train))\n",
    "    split = int(np.floor(valid_ratio * num_train))\n",
    "    \n",
    "    if shuffle:\n",
    "        np.random.seed(random_seed)\n",
    "        np.random.shuffle(indices)\n",
    "        \n",
    "    train_idx, valid_idx = indices[split:], indices[:split]\n",
    "    \n",
    "    train_sampler = SubsetRandomSampler(train_idx)\n",
    "    valid_sampler = SubsetRandomSampler(valid_idx)\n",
    "\n",
    "    train_loader = torch.utils.data.DataLoader(\n",
    "        train_set, batch_size=batch_size, \n",
    "        sampler=train_sampler, num_workers=num_workers\n",
    "    )\n",
    "    valid_loader = torch.utils.data.DataLoader(\n",
    "        valid_set, batch_size=batch_size, \n",
    "        sampler=valid_sampler, num_workers=num_workers\n",
    "    )\n",
    "    return (train_loader, valid_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 生成测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_test_loader(data_dir,\n",
    "                    batch_size,\n",
    "                    shuffle=True,\n",
    "                    num_workers=2):\n",
    "    \n",
    "    # define transform\n",
    "    transform = transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "    ])\n",
    "\n",
    "    dataset = datasets.ImageFolder(root=data_dir, transform=transform)\n",
    "\n",
    "    data_loader = torch.utils.data.DataLoader(\n",
    "        dataset, batch_size=batch_size, \n",
    "        shuffle=shuffle, num_workers=num_workers\n",
    "    )\n",
    "\n",
    "    return data_loader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 测试DataLoaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    8     9     5     1\n"
     ]
    }
   ],
   "source": [
    "train_loader, valid_loader = get_train_valid_loader('./face_train_set/',\n",
    "                                                    batch_size = 4,\n",
    "                                                    random_seed = 123,\n",
    "                                                    valid_ratio = 0.1,\n",
    "                                                    shuffle = True)\n",
    "\n",
    "test_loader = get_test_loader('./face_test_set/',\n",
    "                              batch_size=4,\n",
    "                              shuffle=True)\n",
    "\n",
    "classes = ('0','1', '2', '3', '4', '5', '6', '7', '8', '9')\n",
    "    \n",
    "# get some random training images\n",
    "dataiter = iter(train_loader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# show images\n",
    "img = torchvision.utils.make_grid(images) / 2 + 0.5 # unnormalize\n",
    "npimg = img.numpy()\n",
    "plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "plt.show()\n",
    "    \n",
    "# print labels\n",
    "print(' '.join('%5s' % classes[labels[j]] for j in range(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 定义神经网络: MiniVGG\n",
    "\n",
    "VGG的优点：\n",
    "\n",
    "1.小卷积核，将卷积核全部替换为3x3（极少用了1x1）\n",
    "\n",
    "2.小池化核，相比AlexNet的3x3的池化核，VGG全部为2x2的池化核\n",
    "\n",
    "3.层数更深特征图更宽，基于前两点外，由于卷积核专注于扩大通道数、池化专注于缩小宽和高，使得模型架构上更深更宽的同时，计算量的增加放缓；\n",
    "\n",
    "| **Layer Type** | &nbsp; &nbsp; &nbsp;**Output Size**&nbsp; &nbsp; &nbsp; | **Filter Size/Stride** |\n",
    "|:--------| :-----------|:--------|\n",
    "| INPUT IMAGE | $128 \\times 128 \\times 3$ |  |\n",
    "| CONV | $128 \\times 128 \\times 32$ | $3 \\times 3, K = 32$ |\n",
    "| ACT | $128 \\times 128 \\times 32$ |  |\n",
    "| GN | $128 \\times 128 \\times 32$ |  |\n",
    "| CONV | $128 \\times 128 \\times 32$ | $3 \\times 3, K = 32$ |\n",
    "| ACT | $128 \\times 128 \\times 32$ |  |\n",
    "| GN | $128 \\times 128 \\times 32$ |  |\n",
    "| POOL | $64 \\times 64 \\times 32$ | $2 \\times 2$ |\n",
    "| DROPOUT | $64 \\times 64 \\times 32$ |  |\n",
    "| CONV | $64 \\times 64 \\times 64$ | $3 \\times 3, K = 64$ |\n",
    "| ACT | $64 \\times 64 \\times 64$ |  |\n",
    "| GN | $64 \\times 64 \\times 64$ |  |\n",
    "| CONV | $64 \\times 64 \\times 64$ | $3 \\times 3, K = 64$ |\n",
    "| ACT | $64 \\times 64 \\times 64$ |  |\n",
    "| GN | $64 \\times 64 \\times 64$ |  |\n",
    "| POOL | $32 \\times 32 \\times 64$ | $2 \\times 2$ |\n",
    "| DROPOUT | $32 \\times 32 \\times 64$ |  |\n",
    "| FC | $1024$ |  |\n",
    "| ACT | $1024$ |  |\n",
    "| GN | $1024$ |  |\n",
    "| DROPOUT | $1024$ |  |\n",
    "| FC | $256$ |  |\n",
    "| ACT | $256$ |  |\n",
    "| GN | $256$ |  |\n",
    "| DROPOUT | $256$ |  |\n",
    "| FC | $10$ |  |\n",
    "| SOFTMAX | $10$ |  |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def activation(name):\n",
    "    '''\n",
    "    Activation function switcher\n",
    "    '''\n",
    "    if name.upper() == 'TANH':\n",
    "        return nn.Tanh()\n",
    "    elif name.upper() == 'RELU':\n",
    "        return nn.ReLU(inplace=True)\n",
    "    elif name in ['leaky_relu', 'LeakyReLU']:\n",
    "        return nn.LeakyReLU(inplace=True)\n",
    "    elif name.upper() == 'SIGMOID':\n",
    "        return nn.Sigmoid()\n",
    "    elif name.upper() == 'SOFTPLUS':\n",
    "        return nn.Softplus()\n",
    "    else: \n",
    "        raise ValueError(f'Unknown activation function: {name}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConvBlock(nn.Sequential):\n",
    "    '''\n",
    "    Convolutional Block\n",
    "    '''\n",
    "    def __init__(self, in_channels, out_channels, pool=True, act_name = 'relu',drop_rate=0.):\n",
    "        super().__init__()\n",
    "        self.add_module('conv', nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=True))\n",
    "        self.add_module('act', activation(act_name))\n",
    "        self.add_module('norm', nn.GroupNorm(4,out_channels))\n",
    "        if pool:\n",
    "            self.add_module('pool', nn.MaxPool2d(kernel_size=2))\n",
    "        if drop_rate > 0:\n",
    "            self.add_module('dropout', nn.Dropout(p=drop_rate)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FcBlock(nn.Sequential):\n",
    "    '''\n",
    "    Fully Connected Block\n",
    "    '''\n",
    "    def __init__(self, dim_in, dim_out, act_name = 'relu', drop_rate=0.):\n",
    "        super().__init__()\n",
    "        self.add_module('fc', nn.Linear(dim_in, dim_out, bias = True))\n",
    "        self.add_module('act', activation(act_name))\n",
    "        self.add_module('norm', nn.GroupNorm(4,dim_out))\n",
    "        if drop_rate > 0:\n",
    "            self.add_module('dropout', nn.Dropout(p=drop_rate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self, act_name = 'relu', init_name = 'kaiming_normal', drop_rate = 0.):\n",
    "        super(Net, self).__init__()\n",
    "        model = nn.Sequential()\n",
    "        model.add_module('conv_block1', ConvBlock(3,  32, pool=False))\n",
    "        model.add_module('conv_block2', ConvBlock(32, 32, pool=True,  drop_rate=drop_rate))\n",
    "        model.add_module('conv_block3', ConvBlock(32, 64, pool=False))\n",
    "        model.add_module('conv_block4', ConvBlock(64, 64, pool=True, drop_rate=drop_rate))\n",
    "        model.add_module('flatten',nn.Flatten(start_dim=1, end_dim=-1))\n",
    "        model.add_module('fc_block1', FcBlock(64*32*32, 1024, drop_rate=drop_rate))\n",
    "        model.add_module('fc_block2',FcBlock(1024, 256 ,drop_rate = drop_rate))\n",
    "        model.add_module('last_layer', nn.Linear(256, 10, bias = True))        \n",
    "        self.model = model\n",
    "        \n",
    "        if init_name is not None:\n",
    "            self.init_weight(init_name)\n",
    "            \n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "            \n",
    "    def init_weight(self, name):\n",
    "        if name == 'xavier_normal':\n",
    "            nn_init = nn.init.xavier_normal_\n",
    "        elif name == 'xavier_uniform':\n",
    "            nn_init = nn.init.xavier_uniform_\n",
    "        elif name == 'kaiming_normal':\n",
    "            nn_init = nn.init.kaiming_normal_\n",
    "        elif name == 'kaiming_uniform':\n",
    "            nn_init =  nn.init.kaiming_uniform_\n",
    "        else:\n",
    "            raise ValueError(f'unknown initialization function: {name}')\n",
    "    \n",
    "        for param in self.parameters():\n",
    "            if len(param.shape) > 1:\n",
    "                nn_init(param)\n",
    "                \n",
    "    def model_size(self):\n",
    "        n_params = 0\n",
    "        for param in self.parameters():\n",
    "            n_params += param.numel()\n",
    "        return n_params "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 网络结构可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorboardX import SummaryWriter\n",
    "\n",
    "dummy_input = torch.rand(1,3,128,128)\n",
    "model = Net()\n",
    "\n",
    "with SummaryWriter(comment='Net') as w:\n",
    "    w.add_graph(model,(dummy_input,))# 在同目录下生成runs文件夹\n",
    "    # 在含有runs的目录下使用命令行：tensorboard --logdir runs\n",
    "    # 用浏览器打开生成的xxxx6006地址即可"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"float: center;\" src=\"./jupyter_image/tensorboard.png\" width=\"60%\"> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 打印网络结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (model): Sequential(\n",
      "    (conv_block1): ConvBlock(\n",
      "      (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (act): ReLU(inplace=True)\n",
      "      (norm): GroupNorm(4, 32, eps=1e-05, affine=True)\n",
      "    )\n",
      "    (conv_block2): ConvBlock(\n",
      "      (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (act): ReLU(inplace=True)\n",
      "      (norm): GroupNorm(4, 32, eps=1e-05, affine=True)\n",
      "      (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    )\n",
      "    (conv_block3): ConvBlock(\n",
      "      (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (act): ReLU(inplace=True)\n",
      "      (norm): GroupNorm(4, 64, eps=1e-05, affine=True)\n",
      "    )\n",
      "    (conv_block4): ConvBlock(\n",
      "      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (act): ReLU(inplace=True)\n",
      "      (norm): GroupNorm(4, 64, eps=1e-05, affine=True)\n",
      "      (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    )\n",
      "    (flatten): Flatten()\n",
      "    (fc_block1): FcBlock(\n",
      "      (fc): Linear(in_features=65536, out_features=1024, bias=True)\n",
      "      (act): ReLU(inplace=True)\n",
      "      (norm): GroupNorm(4, 1024, eps=1e-05, affine=True)\n",
      "    )\n",
      "    (fc_block2): FcBlock(\n",
      "      (fc): Linear(in_features=1024, out_features=256, bias=True)\n",
      "      (act): ReLU(inplace=True)\n",
      "      (norm): GroupNorm(4, 256, eps=1e-05, affine=True)\n",
      "    )\n",
      "    (last_layer): Linear(in_features=256, out_features=10, bias=True)\n",
      "  )\n",
      ")\n",
      "num of params: 67443370\n",
      "input shape: torch.Size([2, 3, 128, 128]), output shape: torch.Size([2, 10])\n"
     ]
    }
   ],
   "source": [
    "model = Net()\n",
    "print(model)\n",
    "print(f'num of params: {model.model_size()}')\n",
    "\n",
    "x = torch.randn(2,3,128,128)\n",
    "y = model(x)\n",
    "print(f'input shape: {x.shape}, output shape: {y.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 参数管理\n",
    "\n",
    "使用argparse库创建args对象，对参数集中管理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse\n",
    "\n",
    "class Options(object):\n",
    "    def __init__(self):\n",
    "        parser = argparse.ArgumentParser()\n",
    "        parser.add_argument('--no_cuda', action='store_true', default=False, help='disable CUDA or not')\n",
    "        parser.add_argument('--batch_size', type=int, default=8, help='batchsize for dataloader')\n",
    "        parser.add_argument('--info_print', type=int, default=25, help='when to print loss info')\n",
    "        parser.add_argument('--lr', type=float, default=1e-3, help='initial learning rate')\n",
    "        parser.add_argument('--step_size', type=int, default=2000, help='step size in lr_scheduler for Adam optimizer')\n",
    "        parser.add_argument('--gamma', type=float, default=0.7, help='gamma in lr_scheduler for Adam optimizer')\n",
    "        parser.add_argument('--dropout', type=float, default=0.75, help='dropout rate')\n",
    "        parser.add_argument('--act_name', type=str, default='relu', help='activation function')\n",
    "        parser.add_argument('--init_name', type=str, default='kaiming_normal', help='weight initiate method')\n",
    "        parser.add_argument('--epochs_Adam', type=int, default=10, help='epochs for Adam optimizer')\n",
    "        parser.add_argument('--epochs_LBFGS', type=int, default=2, help='epochs for LBFGS optimizer')\n",
    "        self.parser = parser\n",
    "        \n",
    "    def parse(self):\n",
    "        arg = self.parser.parse_args(args=[])\n",
    "        arg.cuda = not arg.no_cuda and torch.cuda.is_available()\n",
    "        arg.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "        return arg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6 训练过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import shutil\n",
    "import torch.optim as optim\n",
    "from torch.optim.lr_scheduler import StepLR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_model(state, is_best=None, save_dir=None):\n",
    "    '''\n",
    "    save last model and best model\n",
    "    '''\n",
    "    last_model = os.path.join(save_dir, 'last_model.pth.tar')\n",
    "    torch.save(state, last_model)\n",
    "    if is_best:\n",
    "        best_model = os.path.join(save_dir, 'best_model.pth.tar')\n",
    "        shutil.copyfile(last_model, best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Trainer(object):\n",
    "    def __init__(self, args):\n",
    "        self.device  = args.device\n",
    "        print(f'Working device: {self.device}')\n",
    "                \n",
    "        self.info_print = args.info_print\n",
    "        self.model = args.model\n",
    "        self.model_name = self.model.__class__.__name__\n",
    "        self.model_path = self._model_path()\n",
    "        \n",
    "        self.criterion = nn.CrossEntropyLoss()\n",
    "                \n",
    "        self.epochs_Adam = args.epochs_Adam\n",
    "        self.epochs_LBFGS = args.epochs_LBFGS\n",
    "        \n",
    "        self.optimizer_Adam = optim.Adam(self.model.parameters(), lr=args.lr)\n",
    "        self.optimizer_LBFGS = optim.LBFGS(self.model.parameters(), max_iter=20, tolerance_grad=1.e-8, tolerance_change=1.e-12)\n",
    "        self.lr_scheduler = StepLR(self.optimizer_Adam, step_size=args.step_size, gamma=args.gamma)\n",
    "        self.train_loader = args.train_loader\n",
    "        self.valid_loader = args.valid_loader\n",
    "        \n",
    "        self.model.to(self.device)\n",
    "        self.model.zero_grad()\n",
    "    \n",
    "    def _model_path(self):\n",
    "        \"\"\"Path to save the model\"\"\"\n",
    "        if not os.path.exists('checkpoints'):\n",
    "            os.mkdir('checkpoints')\n",
    "\n",
    "        path = os.path.join('checkpoints', self.model_name)\n",
    "        if not os.path.exists(path):\n",
    "            os.mkdir(path)\n",
    "    \n",
    "        return path\n",
    "\n",
    "    def infos_Adam(self, epoch, batch, loss):\n",
    "        infos = 'Adam  ' + \\\n",
    "            f'Epoch # {epoch+1:3d}//{self.epochs_Adam+self.epochs_LBFGS} Batch: {batch+1:3d} ' + \\\n",
    "            f'Loss: {loss:.4e} ' + f'lr: {self.lr_scheduler.get_lr()[0]:.2e} '\n",
    "        print(infos)\n",
    "        \n",
    "    def infos_LBFGS(self, epoch, batch, loss):\n",
    "        infos = 'LBFGS ' + \\\n",
    "            f'Epoch # {epoch+1:3d}//{self.epochs_Adam+self.epochs_LBFGS} Batch: {batch+1:3d} ' + \\\n",
    "            f'Loss: {loss:.2e}'\n",
    "        print(infos)\n",
    "\n",
    "    def valid(self, epoch, batch):\n",
    "        self.model.eval()\n",
    "        valid_loss = 0.0\n",
    "        count = 0\n",
    "        for i, data in enumerate(self.valid_loader):\n",
    "            inputs, labels = data\n",
    "            inputs, labels = inputs.to(self.device), labels.to(self.device)\n",
    "            outputs = self.model(inputs)\n",
    "            loss = self.criterion(outputs, labels)\n",
    "            valid_loss += loss.item()\n",
    "            count = count+1\n",
    "        valid_loss = valid_loss/count\n",
    "        infos = 'Valid ' + \\\n",
    "            f'Epoch # {epoch+1:3d}//{self.epochs_Adam+self.epochs_LBFGS} Batch: {batch+1:3d} ' + \\\n",
    "            f'Loss: {valid_loss:.4e} '\n",
    "        print(infos)\n",
    "        return valid_loss\n",
    "        \n",
    "    def train(self):\n",
    "        print('Start training ...')\n",
    "        \n",
    "        train_losses_index = []\n",
    "        train_losses = []\n",
    "        valid_losses_index = []\n",
    "        valid_losses = []\n",
    "        curve_index = 0\n",
    "        best_loss = 1.e10\n",
    "        \n",
    "        for epoch in range(self.epochs_Adam):\n",
    "            running_loss = 0.0\n",
    "            for batch, data in enumerate(self.train_loader):\n",
    "                inputs, labels = data\n",
    "                inputs, labels = inputs.to(self.device), labels.to(self.device)           \n",
    "                self.optimizer_Adam.zero_grad()\n",
    "                outputs = self.model(inputs)\n",
    "                loss = self.criterion(outputs, labels)\n",
    "                loss.backward()\n",
    "                self.optimizer_Adam.step()\n",
    "                self.lr_scheduler.step()\n",
    "                running_loss += loss.item()\n",
    "                if (batch+1) % self.info_print == 0:\n",
    "                    running_loss = running_loss / self.info_print\n",
    "                    self.infos_Adam(epoch, batch, running_loss)                    \n",
    "                    train_losses_index += [curve_index]\n",
    "                    train_losses += [running_loss]\n",
    "                    running_loss = 0.0   \n",
    "\n",
    "                    valid_loss = self.valid(epoch, batch)\n",
    "                    is_best = valid_loss < best_loss\n",
    "                    best_loss = valid_loss if is_best else best_loss\n",
    "                    state = {'epoch': epoch, \n",
    "                            'state_dict': self.model.state_dict(),\n",
    "                            'best_loss': best_loss}\n",
    "                    save_model(state, is_best, save_dir=self.model_path)\n",
    "                    valid_losses_index += [curve_index]\n",
    "                    valid_losses += [valid_loss]\n",
    "                curve_index = curve_index + 1\n",
    "                \n",
    "        for epoch in range(self.epochs_Adam, self.epochs_Adam + self.epochs_LBFGS):\n",
    "            running_loss = 0.0\n",
    "            for batch, data in enumerate(self.train_loader):\n",
    "                inputs, labels = data\n",
    "                inputs, labels = inputs.to(self.device), labels.to(self.device)  \n",
    "                def closure():\n",
    "                    if torch.is_grad_enabled():\n",
    "                        self.optimizer_LBFGS.zero_grad()\n",
    "                    outputs = self.model(inputs)\n",
    "                    loss = self.criterion(outputs, labels)\n",
    "                    if loss.requires_grad:\n",
    "                        loss.backward()\n",
    "                    return loss\n",
    "                self.optimizer_LBFGS.step(closure)\n",
    "                loss = closure()\n",
    "                running_loss += loss.item()\n",
    "                if (batch+1) % self.info_print == 0:\n",
    "                    running_loss = running_loss / self.info_print\n",
    "                    self.infos_LBFGS(epoch, batch, running_loss)\n",
    "                    train_losses_index += [curve_index]\n",
    "                    train_losses += [running_loss]\n",
    "                    running_loss = 0.0   \n",
    "                    \n",
    "                    valid_loss = self.valid(epoch, batch)\n",
    "                    is_best = valid_loss < best_loss\n",
    "                    best_loss = valid_loss if is_best else best_loss\n",
    "                    state = {'epoch': epoch, \n",
    "                            'state_dict': self.model.state_dict(),\n",
    "                            'best_loss': best_loss}\n",
    "                    save_model(state, is_best, save_dir=self.model_path)\n",
    "                    valid_losses_index += [curve_index]\n",
    "                    valid_losses += [valid_loss]\n",
    "                curve_index = curve_index + 1         \n",
    "                \n",
    "        print('Finished training ...')\n",
    "        # save loss curve info\n",
    "        np.save('loss.npy',[train_losses_index,train_losses,valid_losses_index,valid_losses])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "args = Options().parse()\n",
    "args.model = Net(drop_rate = args.dropout, act_name = args.act_name)\n",
    "args.train_loader, args.valid_loader = get_train_valid_loader('./face_train_set/',\n",
    "                                                              batch_size = args.batch_size,\n",
    "                                                              random_seed = 123,\n",
    "                                                              valid_ratio = 0.1,\n",
    "                                                              shuffle = True,)\n",
    "\n",
    "trainer = Trainer(args)\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制Loss曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load data\n",
    "loss_data = np.load('loss.npy',allow_pickle=True)\n",
    "train_losses_index = loss_data[0]\n",
    "train_losses = loss_data[1]\n",
    "valid_losses_index = loss_data[2]\n",
    "valid_losses = loss_data[3]\n",
    "\n",
    "# loss curve\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_title('Loss curve',usetex = True,fontsize=20)\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_xlabel('batch',usetex = True,fontsize=20)\n",
    "ax.set_ylabel('loss',usetex = True,fontsize=20)\n",
    "ax.plot(train_losses_index, train_losses, color = 'g', label = 'train loss')\n",
    "ax.plot(valid_losses_index, valid_losses, color = 'r', linestyle='--', label = 'valid loss')\n",
    "ax.legend(frameon=False, loc = 'best')\n",
    "plt.savefig('Loss_curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7 测试过程\n",
    "\n",
    "## 7.1 测试模型在测试集上的整体表现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Tester(object):\n",
    "    def __init__(self, args):\n",
    "        self.device  = args.device\n",
    "        print(f'Working device: {self.device}')\n",
    "        \n",
    "        self.model = args.model\n",
    "        model_name = self.model.__class__.__name__\n",
    "        model_path = os.path.join('checkpoints',\n",
    "                                  model_name,\n",
    "                                  'best_model.pth.tar')\n",
    "        best_model = torch.load(model_path)\n",
    "        self.model.load_state_dict(best_model['state_dict'])        \n",
    "        self.model.to(self.device)\n",
    "        self.test_loader = args.test_loader        \n",
    "        \n",
    "    def test(self):\n",
    "        self.model.eval()\n",
    "\n",
    "        correct = 0\n",
    "        total = 0\n",
    "\n",
    "        class_correct = list(0. for i in range(10))\n",
    "        class_total = list(0. for i in range(10))\n",
    "\n",
    "        with torch.no_grad():\n",
    "            for data in self.test_loader:\n",
    "                images, labels = data\n",
    "                images, labels = images.to(self.device), labels.to(self.device)\n",
    "\n",
    "                outputs = self.model(images)\n",
    "                _, predicted = torch.max(outputs, 1)\n",
    "\n",
    "                total += labels.size(0)\n",
    "                correct += (predicted == labels).sum().item()\n",
    "\n",
    "                c = (predicted == labels).squeeze()\n",
    "                for i in range(4):\n",
    "                    label = labels[i]\n",
    "                    class_correct[label] += c[i].item()\n",
    "                    class_total[label] += 1\n",
    "        \n",
    "        classes = ('0','1', '2', '3', '4', '5', '6', '7', '8', '9')\n",
    "        print('Accuracy of the network on test images: %d %%' % (100 * correct / total))\n",
    "        for i in range(10):\n",
    "            try:\n",
    "                print('Accuracy of %5s : %2d %%' % (classes[i], 100 * class_correct[i] / class_total[i]))\n",
    "            except:\n",
    "                print('There is no test image scoring %5s' % classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PS：这个结果对应的模型被我不小心覆盖掉了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working device: cuda\n",
      "Accuracy of the network on test images: 92 %\n",
      "Accuracy of     0 : 95 %\n",
      "Accuracy of     1 : 94 %\n",
      "Accuracy of     2 : 90 %\n",
      "Accuracy of     3 : 85 %\n",
      "Accuracy of     4 : 89 %\n",
      "Accuracy of     5 : 90 %\n",
      "Accuracy of     6 : 90 %\n",
      "Accuracy of     7 : 96 %\n",
      "Accuracy of     8 : 89 %\n",
      "Accuracy of     9 : 97 %\n"
     ]
    }
   ],
   "source": [
    "args = Options().parse()\n",
    "args.model = Net(drop_rate = args.dropout, act_name = args.act_name)\n",
    "args.test_loader = get_test_loader('./face_test_set/',\n",
    "                                   batch_size=8,\n",
    "                                   shuffle=False,\n",
    "                                   num_workers=2)\n",
    "\n",
    "tester = Tester(args)\n",
    "tester.test()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.2 测试模型在随机一个batch上的表现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GroundTruth:      2     9     2     0\n",
      "Predicted:      2     9     2     0\n"
     ]
    }
   ],
   "source": [
    "# load model\n",
    "model = Net()\n",
    "model_name = model.__class__.__name__\n",
    "model_path = os.path.join('checkpoints',model_name,'best_model.pth.tar')\n",
    "best_model = torch.load(model_path)\n",
    "model.load_state_dict(best_model['state_dict'])        \n",
    "model.eval()\n",
    "\n",
    "classes = ('0','1', '2', '3', '4', '5', '6', '7', '8', '9')\n",
    "\n",
    "# initial dataloader\n",
    "test_loader = get_test_loader('./face_test_set/',\n",
    "                              batch_size=4,\n",
    "                              shuffle=True,\n",
    "                              num_workers=2)\n",
    "# define transform\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "    ])\n",
    "\n",
    "dataiter = iter(test_loader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# show images\n",
    "img = torchvision.utils.make_grid(images) / 2 + 0.5 # unnormalize\n",
    "npimg = img.numpy()\n",
    "plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "plt.show()\n",
    "\n",
    "# GroundTruth\n",
    "print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] \n",
    "                               for j in range(4)))\n",
    "# Predicted\n",
    "outputs = model(images)\n",
    "_, predicted = torch.max(outputs, 1)\n",
    "print('Predicted: ', ' '.join('%5s' % classes[predicted[j]] \n",
    "                              for j in range(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8 静态颜值预测\n",
    "\n",
    "可直接从此处开始运行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import torchvision\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from torchvision import transforms, datasets\n",
    "from my_net import Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted:      0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted:      6\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted:      7\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted:      8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted:      7     9     7     8     8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted:      1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted:      9\n"
     ]
    }
   ],
   "source": [
    "classes = ('0','1', '2', '3', '4', '5', '6', '7', '8', '9')\n",
    "\n",
    "model = Net()\n",
    "model_name = model.__class__.__name__\n",
    "model_path = os.path.join('checkpoints',model_name,'best_model.pth.tar')\n",
    "best_model = torch.load(model_path)\n",
    "model.load_state_dict(best_model['state_dict'])        \n",
    "model.eval()\n",
    "    \n",
    "image_list = os.listdir('./predict_set')\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((128,128)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "    ])\n",
    "\n",
    "for image_name in image_list:\n",
    "    image = face_recognition.load_image_file('./predict_set/'+image_name)\n",
    "    face_locations = face_recognition.face_locations(image)\n",
    "    image_container = torch.empty(len(face_locations),3,128,128)\n",
    "    for index,face_location in enumerate(face_locations):\n",
    "        top, right, bottom, left = face_location\n",
    "        image_container[index] = transform(Image.fromarray(image[top:bottom, left:right]))\n",
    "\n",
    "    # show images\n",
    "    img = torchvision.utils.make_grid(image_container) / 2 + 0.5 # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "    \n",
    "    # Predicted\n",
    "    outputs = model(image_container)\n",
    "    _, predicted = torch.max(outputs, 1)\n",
    "    print('Predicted: ', ' '.join('%5s' % classes[predicted[j]] for j in range(len(face_locations))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"float: center;\" src=\"./jupyter_image/predicting.png\" width=\"60%\"> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9 实时颜值预测\n",
    "\n",
    "可直接从此处开始运行\n",
    "\n",
    "**Step1**: 使用OpenCV库获取帧\n",
    "\n",
    "**Step2**: 使用face_recognition库获取人脸\n",
    "\n",
    "**Step3**: 使用torchvision.transform进行与前面相同的预处理\n",
    "\n",
    "**Step4**: 使用训练好的模型进行预测\n",
    "\n",
    "**Step5**: 使用OpenCV将训练结果打在帧上并显示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import face_recognition\n",
    "import cv2\n",
    "import os\n",
    "import torch\n",
    "from PIL import Image\n",
    "from torchvision import transforms, datasets\n",
    "from my_net import Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define transforms\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize(128),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "    ])\n",
    "\n",
    "classes = ('0','1', '2', '3', '4', '5', '6', '7', '8', '9')\n",
    "\n",
    "# load model\n",
    "model = Net()\n",
    "model_name = model.__class__.__name__\n",
    "model_path = os.path.join('checkpoints',model_name,'best_model.pth.tar')\n",
    "best_model = torch.load(model_path)\n",
    "model.load_state_dict(best_model['state_dict'])        \n",
    "model.eval()\n",
    "\n",
    "# camera prepare\n",
    "video_capture = cv2.VideoCapture(0)\n",
    "process_this_frame = True\n",
    "while True:\n",
    "    # get a frame from camera\n",
    "    ret, frame = video_capture.read()\n",
    "    try:\n",
    "        # resize the frame to speed up the process\n",
    "        small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)\n",
    "        # transform to RGB image\n",
    "        rgb_frame = small_frame[:,:,::-1]\n",
    "        if process_this_frame:\n",
    "            face_locations = face_recognition.face_locations(rgb_frame)\n",
    "            face_container = torch.empty(len(face_locations),3,128,128)\n",
    "            for index,face_location in enumerate(face_locations):\n",
    "                top, right, bottom, left = face_location\n",
    "                face_container[index] = transform(Image.fromarray(rgb_frame[top:bottom, left:right]))\n",
    "            # predict face rank\n",
    "            outputs = model(face_container)\n",
    "            _, face_ranks = torch.max(outputs, 1)\n",
    "        process_this_frame = not process_this_frame\n",
    "        # show the face ranks on the screen\n",
    "        for (top, right, bottom, left), rank in zip(face_locations, face_ranks):\n",
    "            top = top * 4\n",
    "            right = right * 4\n",
    "            bottom = bottom * 4\n",
    "            left = left * 4\n",
    "            cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255),  2)\n",
    "            cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), 2)\n",
    "            font = cv2.FONT_HERSHEY_DUPLEX\n",
    "            cv2.putText(frame, 'Rank:'+ classes[rank], (left+6, bottom-6), font, 1.0, (255, 255, 255), 1)\n",
    "    except:\n",
    "        # in case that no face is captured\n",
    "        pass\n",
    "    cv2.imshow('Video', frame)\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "        # press 'q' to close the window\n",
    "        break\n",
    "\n",
    "video_capture.release()\n",
    "cv2.destroyAllWindows()"
   ]
  }
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